Fairis shows that weighting a client by a security parameter minus its local fairness score makes its aggregation weight strictly decrease with reported bias, while keeping every client's weight positive.
Official Journal of the European Union, L series (2024), regulation (EU) 2024/1689
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Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
Fairis shows that weighting a client by a security parameter minus its local fairness score makes its aggregation weight strictly decrease with reported bias, while keeping every client's weight positive.